Evidence map›Paper›PMID 37250646›Full record

ArticleFrontiers in medicine2023

Advantages of digital technology in the assessment of bone marrow involvement in Gaucher's disease.

Esther Valero-Tena, Mercedes Roca-Espiau, Jose Verdú-Díaz, Jordi Diaz-Manera, Marcio Andrade-Campos, Pilar Giraldo

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Gaucher disease, state of the art and perspectives.Journal of internal medicine · 2025
    Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Esther Valero-TenaDepartamento de Medicina Interna y Reumatología, Hospital MAZ, Zaragoza, Spain.
Mercedes Roca-EspiauFundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras Lisosomales (FEETEG), Zaragoza, Spain.
Jose Verdú-DíazJohn Walton Muscular Dystrophy Research Center, Newcastle University, Newcastle upon Tyne, United Kingdom.
Jordi Diaz-ManeraJohn Walton Muscular Dystrophy Research Center, Newcastle University, Newcastle upon Tyne, United Kingdom.
Marcio Andrade-CamposFundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras Lisosomales (FEETEG), Zaragoza, Spain.
Pilar GiraldoFundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras Lisosomales (FEETEG), Zaragoza, Spain.
Fundación Española para la Ciencia y la Tecnología · ESMuscular Dystrophy UK · GBNewcastle University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gaucher disease (GD) is a genetic lysosomal disorder characterized by high bone marrow (BM) involvement and skeletal complications. The pathophysiology of these complications is not fully elucidated. Magnetic resonance imaging (MRI) is the gold standard to evaluate BM. This study aimed to apply machine-learning techniques in a cohort of Spanish GD patients by a structured bone marrow MRI reporting model at diagnosis and follow-up to predict the evolution of the bone disease. In total, 441 digitalized MRI studies from 131 patients (M: 69, F:62) were reevaluated by a blinded expert radiologist who applied a structured report template. The studies were classified into categories carried out at different stages as follows: A: baseline; B: between 1 and 4 y of follow-up; C: between 5 and 9 y; and D: after 10 years of follow-up. Demographics, genetics, biomarkers, clinical data, and cumulative years of therapy were included in the model. At the baseline study, the mean age was 37.3 years (1-80), and the median Spanish MRI score (S-MRI) was 8.40 (male patients: 9.10 vs. female patients: 7.71) (

Indexed as

bone diseasebone marrow MRIGaucher diseasepredictive factorsrandom forest machine-learning study

Identifiers

PMID37250646
PMCPMC10213682
OpenAlexW4376279551

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.